Genome Analysis: Data Accuracy, Haplotyping, and Mapping

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DESCRIPTION (provided by applicant): Long-term objective: to develop quantitative methods and software for the interpretation and analysis of human genetic variation. The methods will be tailored to the specific needs of large-scale studies of sequence variation, particularly those attempting to understand the genetic basis of complex diseases. The aim will be to supply scientists involved in such studies with an integrated set of tools to a) monitor and improve data quality, b) design effective studies, and c) perform powerful data analyses, ultimately reducing the cost of developing effective medical treatments for common diseases.Major specific aims: 1. To develop automatic methods for calling genotypes from sequence trace data, and for assigning each genotype call a "quality score", quantifying the probability that the call is correct, allowing data accuracy to be carefully monitored. 2. To extend an existing statistical method for inferring haplotypes from population genotype data to allow it to impute missing genotypes, identify potential genotyping errors, and make it more applicable to data on a larger (genomic) scale. 3. To develop methods to infer recombination rates, and identify potential recombination "hotspots" or "coldspots", from population data (information that will aid in the design of effective mapping studies aiming to locate variants affecting disease susceptibility). 4. To develop methods for linkage disequilibrium mapping that make efficient use of data from many SNP markers simultaneously, thus reducing the costs, and increasing the chances of success, of mapping studies. Aim 1 will be achieved through a statistical analysis of pertinent sequence trace features for analyst-called genotypes. Aims 2-4 will exploit population genetics models that make predictions about patterns of haplotypic variation expected in natural populations, and how patterns of linkage disequilibrium will be affected by variations in local recombination rate. Computational statistical methods, such as Markov chain Monte Carlo, will be used extensively in implementing these methods. The methods will be tested on real and simulated data. User-friendly software will be developed, documented, distributed and supported.